wie_nutzt_man_while_schleifen.ipynb

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 "cells": [
  {
   "source": [
    "# Wie nutzt man While-Schleifen?\n",
    "## Daten laden"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[37.92655435 23.90101111  1.        ]\n [35.88942857 22.73639281  1.        ]\n [29.49674574 21.42168559  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "feature = np.array([[37.92655435, 23.90101111],\n",
    "                    [35.88942857, 22.73639281],\n",
    "                    [29.49674574, 21.42168559],\n",
    "                    [32.48016326, 21.7340484 ],\n",
    "                    [38.00676226, 24.37202837],\n",
    "                    [30.73073988, 22.69832608],\n",
    "                    [35.93672343, 21.07445241],\n",
    "                    [38.65212459, 20.57099727],\n",
    "                    [35.52041768, 21.74519457],\n",
    "                    [37.69535497, 20.33073640],\n",
    "                    [33.00699292, 22.57063861],\n",
    "                    [33.73140934, 23.81730782],\n",
    "                    [43.85053380, 20.05153803],\n",
    "                    [32.95555986, 24.12153986],\n",
    "                    [36.38192916, 19.20280266],\n",
    "                    [36.54270168, 20.45388966],\n",
    "                    [33.08246118, 22.20524015],\n",
    "                    [31.76866280, 21.01201139],\n",
    "                    [42.24260825, 20.44394610],\n",
    "                    [29.04450264, 22.46633771],\n",
    "                    [30.04284328, 21.54561621],\n",
    "                    [18.95626707, 19.66737753],\n",
    "                    [18.60176718, 17.74023009],\n",
    "                    [12.85314993, 18.42746953],\n",
    "                    [28.62450072, 17.94781944],\n",
    "                    [21.00655655, 19.33438286],\n",
    "                    [17.33580556, 18.81696459],\n",
    "                    [31.17129195, 17.23625014],\n",
    "                    [19.36176482, 20.67772798],\n",
    "                    [27.26581705, 16.71312863],\n",
    "                    [21.19107828, 19.00673617],\n",
    "                    [19.08131597, 15.24401994],\n",
    "                    [26.69761925, 17.05937466],\n",
    "                    [4.44136559 , 3.52432493 ],\n",
    "                    [10.26395607, 1.07729281 ],\n",
    "                    [7.39058439 , 3.44234423 ],\n",
    "                    [4.23565118 , 4.28840232 ],\n",
    "                    [3.87875761 , 5.12407692 ],\n",
    "                    [15.12959925, 6.26045879 ],\n",
    "                    [5.93041263 , 1.70841905 ],\n",
    "                    [4.25054779 , 5.01371294 ],\n",
    "                    [2.15139117 , 4.16668657 ],\n",
    "                    [2.38283228 , 3.83347914 ]])\n",
    "\n",
    "feature = np.concatenate((feature, np.ones(43).reshape(43,1)), axis=1)\n",
    "print(feature[0:3,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(43,)\n"
     ]
    }
   ],
   "source": [
    "labels = np.concatenate((np.ones(21), np.zeros(22)))\n",
    "print(labels.shape)"
   ]
  },
  {
   "source": [
    "## Funktionen Definieren"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize(feature, labels, w, x=None, w_old=None): \n",
    "    # Hier werden x und w_old als None definiert, sodass man sie beim Aufrufen nicht \n",
    "    # unbedingt angeben muss. Sie könnten auch als [1,2,3,4] o.Ä. definiert werden, \n",
    "    # aber None macht es einfacher für die if-Statements\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    plt.title('Trainingsdaten')\n",
    "    plt.xlabel('Grösse [cm]')\n",
    "    plt.ylabel('Länge [cm]')\n",
    "    plt.scatter(feature[:,0], feature[:,1], c=labels)\n",
    "\n",
    "    # Linie\n",
    "    x0 = np.array([min(feature[:,1]), max(feature[:,0])])\n",
    "    if w[1] != 0:\n",
    "        x1 = -(w[0] * x0 + w[2]) / w[1]\n",
    "        plt.plot(x0, x1, label='Gewichte')\n",
    "        if w[1] > 0:\n",
    "            ax.fill_between(x0, x1, x1+1, alpha=0.2,label='Hund')\n",
    "        else:\n",
    "            ax.fill_between(x0, x1, x1-1, alpha=0.2,label='Hund')\n",
    "\n",
    "    if x is not None:\n",
    "        plt.scatter(x[0], x[1],c='r',marker='x',label='Falsch klass.')\n",
    "    \n",
    "    if w_old is not None and w_old[1] != 0:\n",
    "        x1_new = -(w_old[0] * x0 + w_old[2]) / w_old[1]\n",
    "        plt.plot(x0, x1_new, 'r',label='Alten Gewichte')\n",
    "        if w_old[1] > 0:\n",
    "            ax.fill_between(x0, x1_new, x1_new+1, alpha=0.2)\n",
    "        else:\n",
    "            ax.fill_between(x0, x1_new, x1_new-1, alpha=0.2)\n",
    "\n",
    "    ax.set_facecolor('#F6F6F6')\n",
    "    fig.patch.set_facecolor('#F6F6F6')\n",
    "\n",
    "    plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1.1))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "def perzeptron(w, x):\n",
    "    if np.dot(w, x) > 0:\n",
    "        return 1.0\n",
    "    else:\n",
    "        return 0.0"
   ]
  },
  {
   "source": [
    "## Training"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# gewichte zufällig initialsieren\n",
    "np.random.seed(5) # damit die 'zufälligen' Zahlen immer gleich sind\n",
    "w = np.random.rand(feature.shape[1])\n",
    "\n",
    "visualize(feature, labels, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Es wurden 1 Datenpunkt(e) falsch klassifiziert.\n"
     ]
    }
   ],
   "source": [
    "fehler = 0\n",
    "for x, label in zip(feature, labels):\n",
    "    delta = label - perzeptron(w, x)\n",
    "    w_old = w.copy()\n",
    "    w += (delta * x)\n",
    "    if delta != 0: # falsch klassifiziert\n",
    "        fehler += 1\n",
    "        visualize(feature, labels, w, x, w_old)\n",
    "\n",
    "print(\"Es wurden \" + str(fehler) + \" Datenpunkt(e) falsch klassifiziert.\")\n",
    "\n",
    "# Genauso geht: \n",
    "# print(\"Es wurden {} Datenpunkt(e) falsch klassifiziert.\".format(fehler))"
   ]
  },
  {
   "source": [
    "## While-Schleifen"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "iter = 0\n",
    "while iter < 100:\n",
    "    iter += 1\n",
    "    if iter > 90:\n",
    "        break\n",
    "    elif iter > 20:\n",
    "        continue\n",
    "else:\n",
    "    print(\"Alle Iterationen wurden durchgeführt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Alle Iterationen wurden durchgeführt\n"
     ]
    }
   ],
   "source": [
    "iter = 0\n",
    "while iter < 100:\n",
    "    if perzeptron(w, x) == 1:\n",
    "        print(\"Die Schleife wurde abgebrochen.\")\n",
    "        break\n",
    "    iter += 1\n",
    "else:\n",
    "    print(\"Alle Iterationen wurden durchgeführt\")"
   ]
  },
  {
   "source": [
    "### Perzeptron Lernregel"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(5) # w zurücksetzen\n",
    "w = np.random.rand(feature.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Es wurde keine Lösung gefunden.\n"
     ]
    }
   ],
   "source": [
    "iter = 0\n",
    "max_epochs = 100\n",
    "fehler = np.zeros(max_epochs)\n",
    "\n",
    "while iter < max_epochs:\n",
    "    for x, label in zip(feature, labels):\n",
    "        delta = label - perzeptron(w, x)\n",
    "        w += (delta * x)\n",
    "        if delta != 0:\n",
    "            fehler[iter] += 1\n",
    "    \n",
    "    if fehler[iter] == 0:\n",
    "        visualize(feature, labels, w)\n",
    "        break\n",
    "\n",
    "    iter += 1\n",
    "else:\n",
    "    print(\"Es wurde keine Lösung gefunden.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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3F5/J8zatX7T9n+94mK//YHzF+z328LW889xnIy1MeLBn/wzvv3En0z3ql/m5TYfye2c9a9G2B/c8zYdu+T61uZW1DEsD4tKXnsiJR2c7+3+SQLABuDFcr/gs4HksTEjXVrhw/Q7gWcDHzOyOhqdsBB6J3d8VblsUCCRtJWgxcNxxxyV568Tue3ySa771CBvXD3PI4NLm/XI8+pMpHt07tSQQXLtjF7d87wlOOPKQjvbvOjNTm+dHTx7gl046mnOeu6G+fc/+WT75jQc5amSQ9Wsry97v6HCZl590zJLtP3/iEXzzh3v44fj+Jq9qrzpnPLjnac786TF+5dRN9e1Pz9T4+G0/ZGxkkMNWUNZuu3/3fo45dGhJIPi7r/2Qp56eZcOhQy1e2dreA1VuvvdxLv7Fn+KwWEC//YEn+fy3H2XzEWtZ06TPJkt79s/y5ft2LwkEt+58gn+7+8c866h1DKjFi9v4/hP7+emjR3IRCK4Dtkh6FvAp4Hrgn4HzDvZCM5sDTpG0Hvi8pOea2b2xpzT70zSb8voK4AqALVu2pFqvnghbAh+56BS2bD68o3298bPf5t5H9y19j+kqzzxyHf/+5jM72r/rzCNPHeAX/uYrTEzXFm2fmA4+A+9+xXOWBPFOnP3sozn72Uev6LVP7p/hBX/+H0y2KOtb/udP85rT0q0UZeH0v7iVyemlre2JqSoXnvIM/vyVP7fsfV67Yxdv/Ze7mZyuLQoEE1PB3+qarWdwzAoCTCcu//IP+MCXvh+OIFuoUE5MVZHgS286k4EVRILn/PEXm/790pYkbM6bWQ34FeDDZvZmglZCYma2F7iNIMUUt4tgCczIJuDHy9l3p6Iv1uhw57Wr0aFyPbAseo+pWtPUgeuu6H/c+D+K7o8O5aeGPTLUqqzByS5PZW1ndLhcL3PEzJiYrq34GEaHgu/SRMMJcuG73P3vWvTZWhq4a4wMllcUBIL9Lv37ZSFJIKhKeg3wOuCGcNtB/4OSxsKWAJKGgZcB9zU87XrgdeHoodOBfd3sH4B0v1ijwxUmpqtYQ2fAxHS1MF/c1WxksIxEkxZB+BnIUbBeUx5guFLK1cluJUaHKkuO4cDsHHPztuLKV7uAXh4Qw5XOUrwrKlPLwF3tqJLZ7O+XhSSB4PXAGcD7zexBSScAn0nwug3AVyTdA3wLuMXMbpD0BklvCJ9zE/AAcD/w98DvLfsIOpTmF2t0qEJ1zphumGhsYrqzD4NLx8CAWDe4tNWWxxYBNK8N5rWsrUSVo7j6d27FLYLKov3E9zs6XFnUgdwt0fmjWdqxk/9Vs79fFg569jOz7wG/H7v/IPBXCV53D7DkCmQz2xa7bcAlSQubhTRrEQsfhirDa+J5wlq9Oet6q1kNK830YJqKVNZWRofK3L+7MZh11gKrf8+WBMnefc9atwg6SwuPDpXZs3+2o7IlkWSKiRMlXSvpe5IeiH4yL1mXpFmLiD4M8c6d+Xlj0lsEuTE6XFlyAonyunmrZY8OV5bmnOupzGJULIJjWHxynOy0RTDcvkXQC23LVIAWQZLU0D8SXOhVA15CMHT0n7IsVDelWYuIPgz7Yieap2drzFv+TjL9anSovPTLOlWlUhJDTS4K66VWZYWFzuS8C1o1tUX9Zp22atatadHXM9W7vriFFsHSSkanfQSNlYEsJPnkD5vZrYDM7Edm9iesoimo06xFNBvNMJnDjsh+1rSWHdbaepFbbidovTTUpmdqDFdKXR8nv1Kjw2Xm5o0DswsXeXXaqhkYECNN+nqCk26PUkPh+za2fjoNTkE/0dIBKGlL8lebljQA/EDS/wEeBY7KtFRdNNnBMLZGzUYzdNox5tI1OlRhYmpi0bYgj5u//09Um44LRqEUp1IxEuvYPWRwcUWpo5pyi07oXn3PhislygNaVKa5eWNyprPgNDJUoTZvTFXnWLsmu/97kmrFm4C1BB3GLwB+E/itzErUZWl+sRZGMyx8eRc6xvJ3oulHo8NN0i3T1Vzm3JvVBos2FLlZymQhvdVJJ+rSvp5eBnRJS/qf9qfQ99Qq5ZS2JKOGvhXe3E8wlHRVmZiuMjKYzocn+mAvahGk8KF36RkZqrB/psb8vNUv8pmYquYy596sNjgxVSvUZyk+ki4yMV1jsDzQdMbWpEYa+k9ma/NMVecYGezd36axTNHtjgJe7O+X5dXS7dYs/jeaTPcQMbMLMilRl6V51e9QmLtt9mEoUi1uNRsdKmMW5NoPHV5owXV7SoIk4rXBeiCYri6aMC/vmg2r7PQiKwha2I88daB+fzKFdFOnglbKwnFGE1l22lkMS4elpq3dGfADmb5zDkS1iDRP0o1N1okUPgwuPfF+nENjt/MYqJvVBiemqmw+ojiTFzYbVplGKq5xNE0erg4P0o7xMnVeCWw1LDVt7f5qa8zslmYPSPpr4KvZFKl7sqhFNOagow9GkZrzq1mzq1LzeuV309p0D0fGrER9JN3U4n6zzlsE5aYp2F4G9NGhCrsnFmaa7fTCuWCfzS+eS1u7zuKPSXpFfIOkAUmfBk7OtFRdkkUtorF5ODFVZe2aEpVSMYb7rXaNV6XO1OaYrs7ntLN4cdAys9y2XlppNnleGh3eo0MVJmdqzM1bfZ+Qg9RQymnhbrUI2p2dfgn4oKRfgfrEcdcTLFP5y5mWqkuyqEUEw9oWNw+L9MVd7RpbBAvXeeTvf9RYG5yqzlHrYLK2Xmg2eV5afQSwMDInD7OyNs4NlUZauNkAlCy0rAaZ2UOSXgb8u6SjCIaN3mFmb8m0RF2URS1idKjMrlgnlk9BnS+HNlzrkYeUQiuNtcE8nOxWYskJcrrzq/njF28eurYS+y73sI9gqMJUdY7Z2nw4aKSGREcjmQbLJYYqA0uuJ0lbyxZBuJD8UcDbgPcTrCT2GUmnrpZF5rP4YnmLIN8ar/XI85XfUW0wKmMeTnYrEaRxFtJbacy9tTRI9j6gL6xJUK3/XtfBWgT1/Q4tvcI8be0+UR+M3b4HODq2zVgF00xk8cVqlic8aiR/QxP71bqGpnaeh/fWa4MFaL20E7/Qaro6T3XOUukjgIXK3MR0ldKAWLum+2sR1MsUm4r6iHWD4Txm6a1zkqV2qaGXZPrOOZBNH0GZ2do809U5hiolJqZqPGusWDW41awUzVPTmG7Jad49XrHIQ4foSowMlXnq6WAq5bQqX40XqkWTR/ZyvqjGUV5pjUYbGSpnPvFcXw9lyaIW0dgZmdehif0sXkPNc4sAGspasCmoI/HURlqVr6xOup1olq5K43/VjdRQZoFA0rGSviJpp6TvSrq0yXPOkrRP0l3hz3uyKk8zWdQiFi5YqhVyuF8/iE8FsDCyI58n1/hU1EVtEcQvtErrGBZOulGQ7P33bGm6Kp25jxr7HbOQ5ae/BvyBmd0paQTYIemWcMWzuK+b2fkZlqOlLGoR8dEMT8/OBWsR5PQk06/i0ztPTPdundskRocr/CRKqxR03qqoRhtUjNJp1dTXn67/H3s/Om9puqrK6IbRzvc7tHTK7bQlWaFMkl4b1dYlHSfptIO9zsweM7M7w9uTwE5gY6cFTlMWtYj4FAZF7dxb7eLTO0dXueZtLYLIorJO1xiqdDZZWy+MDi9MnpdWi6C+/vSiNExeWgTxdFUKqaGwszjLNQmSpIY+TrB4/WvC+5PAx5bzJpI2E6xffEeTh8+QdLekmyWd1OL1WyVtl7R9fHx8OW/d1sR0+jM5LrQIarHZBz0Q5Em8hjUxXc11DXskXtaczpJ6MPGUSZqtmvi8Xnn4P65dU6IUrkkwP2/sn6ml8v8aHapQnTOmq/MplLK5JIHghWZ2CTANYGY/Ibi6OBFJ64DrgDeZ2UTDw3cCx5vZycBHgS8024eZXWFmW8xsy9jYWNK3PqhMWgSxWkGex6j3s/g6unmoSbYTrw1OpnAhVi/EUyb1aV1S+JvH+3rSXGBqpSTVR/hMztQwS6djv9lU3mlLEgiqkkqEU1JLGgMShSZJFYIgcLWZfa7xcTObMLP94e2bgIqkI5MWvlNZLG0XHzngqaF8Gh0qMxmuSZCH3HI78dpgHkbGrES8cjQxVWVNeYChFPpkor6e6tw8B2bncvG3ifpD0px1uBtTUScJBJcBnweOkvR+4BvAXxzsRQqSrp8CdprZh1o855jweYT9DgPAkwnL3rEsrvodLA+wpjQQNIMLOspjtRsdrmAG+2drBWgRxGrTOS9rK4sqRyl+56L+k3rLOwetpWiEVJrDkrsx8VySFcqulrQDeCkg4JVmtjPBvl9MMD/RdyTdFW57F3BcuN9twKuAiyXVgCngIst6leZQVrWIYMm6cvjFzc8H1C1YVEPN+RQgi8ta47gCrUUQiU+el+bcW6PDZXY+lm7tu1MLLYL00sLdmIo6aSl/AExEz5d0nJk93O4FZvYNgsDR7jmXA5cnLEOqsqxFRItmLHSM9f4D6hbEp6LOIj2Ypvh4+bQuUOq2+Bw8abcIon1G93ttdKjCA3v2L6x1slpaBJLeCLwXeAKYIzi5G/C8zErVBVnWIkbC3OXEdJXhcPlKlx/Rl/Opp2eDVmEOTiCtjMbmRipqH8HIopF0C0uEdmp0OFiTYO+BHLUIwplWo07xNI61G1NRJ6leXAr8jJl1LXffDVnWIqKrQX0K6nyKThiP7j2w6H4eRWXbPTmdymRtvTBYLjFYDibPm5yqcuxhw6nsN1p/+rF9U8H9HHzXormh0hwo0jhjbhaSVFUfAfZlVoIeyXKysdFYi6CIX9zVLvqf7PpJfk4grRSprO1Ew2DTbNVE+6n/bXLwXRsdrnBgdq4+yd66FFJ5Q2FWoSctAknRAjQPALdJuhGYiR5vNRKoKLKc2z0azVDUpvxqF/3P83QCaSVKCxShrO2MDpXZF3aiptlHAPEg2fu/TZTKe3TvFCODZUodrkWwsN9s5xtqdxYcCX8/HP6sYRkXkuVdlmP8o4W1J6ZqHLlu1fzJVo11g9HJNf+poaFKkFYpQlnbGR2uMD45w+zcfKqjhiD4Pw4IDunhWgSRhVbKgXRXPhwu96az2Mzel9m75kCWY/xHhyrM1OYZn5zhmWPFG+632pVLA6wbLBemlj06XImVtaCpoaEK339isn47rX1C0CLIy3xR8TKl1Ske7benF5RJukXS+tj9wyT9e2Yl6pKJqVpmtYgouDwxOZ37k0y/Gh0q8/jEdHA753n3xWUt5udpdLiS+jFEJ9rHJ/LzPRuNlynVFkG2qaEkncVjZrY3uhPONXRUZiXqkih/n0UtIqq1mU9BnVvR1cVQjBZBUcraSjTCJ7qdzj6Dv0WevmdROYJ5htJsEZSZ7PEUE3OSjovuSDqecN6hIgtmcszmwxP/APjFZPkU/e97vc5tEiOLPk/5OOEtV7x2nNZ3Ij4iZ2QwH9+z+LGlmcbLet3iJCV9N/ANSV8N758JbM2sRF0ykeFshfHaSVFrcKtd9H8Z6fE6t0lEJ5S0Jmvrhfj34NCUau+lcE2C/TP5uV4nfvJPNTUUTrltZpl8XpPMNfRFSacCpxNcVfxmM9uTekm6LMsJvOL7zcsH1C0WfUmLEKiLVNZW4i2ZtFMm+2d6PwV15JA1ZQZEsDJhqi2CMrNz88zU5jOpDCTpLH4xMGVmNwCHAu8K00OFltbqQc3EawJ5+YC6xaIvaRECdfQZKkJZW1n0nUi5EzXtfXZiYED19FCaZYr2mdXIoSR9BJ8ADkg6GfhD4EfAVZmUpovSvLCl0eIWQT4+oG6xItWyowBQhLK2Uk9vlQYYTHHurXqQzNHfJov/V3wt9Cwk+Y/UwqmhLwQuM7OPsHCxWWFledXvUGWASinI4xV13Pdql8cTSCujGdQwu22h5p5un0z9pJuj1lIWLbjo77cvo6mokwSCSUnvBF4L3BiuVlbcTyRQi9YiyOgkIGlVfHlXszyeQFpZaL3kv6ytZBV48xjQsyjTwsRzvWsRvJpgjqHfMbPHgY3A3x7sRZKOlfQVSTslfVfSpU2eI0mXSbpf0j1hp3TmurGWcPTlLepwv9UujyeQVhb6M/Jf1lai79pIyseQtz4CiFcy0itTNNIqqz6CJKOGHgc+FLv/MMn6CGrAH5jZnZJGgEZWBy4AAAw/SURBVB2SbjGz78Wecy5wYvjzQoL+iBcuo/wr0o2FLEaHygxVBhgsF3O432qXxxNIK0Xqz2hlIfCmvEZ4FCRzVOHKtkWQTWqo3eyjkzS/cEyAmdloux2b2WPAY+HtSUk7CVoT8UBwIXBV2Adxu6T1kjaEr81MllNQR0aHK4X+4q52WZ2YsrAaRg1FUymn/Z3LY0CP94ekvc/JjFJD7SadS61DWNJm4PnAHQ0PbSRY7yCyK9y2KBBI2kp4Edtxxx1Hp6IWQZZpm43rh+spKJc/R48OUhoQz1ifziIpWRpbN8ia0gAbC1DWdjatH079GDauH6ZSEmMjg6nutxMb1w8zMlSuz3KbhsHyAGtKA5mtW6yka8VLOgoYiu4fbM3i2OvWAV8F3m9mn2t47EbgL8P1jZF0K/A2M9vRan9btmyx7du3JypzKzd/5zEuvvpObr70F3jOhrYNmxXbP1NjtjbP4Yf4NNR59chTB9i4fpiBlOaMz9Kunxxgw6HDqc1v3wvjkzMcMlhi7Zr0TpDz88aje6c49vC1qe2zUzO1OX7ydJVjDh06+JOX4fYHnmTj+uEVH6ukHWa2pdljSdYsvgD4IPAMYDdwPLATOCnBayvAdcDVjUEgtAs4NnZ/E/Djg+23U1lOQR1ZN1iG/FRSXBN5OnkczKbDilPWVrKotQ8MKHf/x8FyiWMOTb9v8PRnHpH6PiNJRg39GcH0Et83sxOAlwL/72AvUjBY+FPAzjarmV0PvC4cPXQ6sC/r/gGI9REUID/snHNZS3ImrJrZk5IGJA2Y2Vck/XWC170Y+E3gO5LuCre9CzgOwMy2ATcB5wH3AweA1y/7CFZgYrqKFMwL4pxz/S7JmXBvmOf/GnC1pN0EQ0PbCvP+bROa4WihS5IUNE0TU1VGBsuFyA0751zWWqaGYmsQXEhQW38z8EXgh8AvZ1+07ExM13I13Mw553qpXYvgC8CpZva0pOvM7FeBK7tUrkxlOQW1c84VTbvO4nje5JlZF6SbspyC2jnniqZdILAWtwtvMsPVyZxzrmjaVYtPljRB0DIYDm9Dwikm8mxiKrspqJ1zrmjaTTGxamdLy3K9YuecK5r0lgoqiNrcfK4Wu3bOuV7ru0Cwfya6qthbBM45B30YCLoxBbVzzhVJ/wWC+qI0nhpyzjnox0Awlf3Mo845VyT9Fwi6sEylc84VSf8FgrCPwBeVd865QP8Fgi4sSuOcc0XSh4GghgQjKa4n6pxzRdZ/gWCqyjpfi8A55+oyCwSS/kHSbkn3tnj8LEn7JN0V/rwnq7LETUz7FNTOOReXZX7k08DlwFVtnvN1Mzs/wzIsMTHli9I451xcZi0CM/sa8FRW+1+poEXg/QPOORfpdR/BGZLulnSzpJNaPUnSVknbJW0fHx/v6A19CmrnnFusl4HgTuB4MzsZ+CjB0phNmdkVZrbFzLaMjY119Ka+KI1zzi3Ws0BgZhNmtj+8fRNQkXRk1u8btAg8NeScc5GeBQJJx0hSePu0sCxPZvmec/PG5Iy3CJxzLi6zqrGkzwJnAUdK2gW8F6gAmNk24FXAxZJqwBRwkZllujby/mmfgto55xplFgjM7DUHefxyguGlXeNTUDvn3FK9HjXUVT7PkHPOLdVfgcBnHnXOuSX6KxD4WgTOObdEfwWCcHWyQz015Jxzdf0VCKJRQ94icM65uv4KBGGLYJ33ETjnXF1/BYLpKiODZUq+FoFzztX1VyDwKaidc26J/goE01UfOuqccw36KxD4FNTOObdEfwUCn4LaOeeW6KtAMDntU1A751yjvgoEE1O+cL1zzjXqm0AwH61F4H0Ezjm3SN8Egv2zNcx8CmrnnGvUN4EguqrYU0POObdYZoFA0j9I2i3p3haPS9Jlku6XdI+kU7MqCyxMQe2dxc45t1iWLYJPA+e0efxc4MTwZyvwiQzL4lNQO+dcC5kFAjP7GvBUm6dcCFxlgduB9ZI2ZFWeemrIO4udc26RXvYRbAQeid3fFW5bQtJWSdslbR8fH1/Rmx2xbg3nPvcYxkYGV/R655xbrXqZMG82Bag1e6KZXQFcAbBly5amzzmYFxx/OC84/vCVvNQ551a1XrYIdgHHxu5vAn7co7I451zf6mUguB54XTh66HRgn5k91sPyOOdcX8osNSTps8BZwJGSdgHvBSoAZrYNuAk4D7gfOAC8PquyOOecay2zQGBmrznI4wZcktX7O+ecS6Zvrix2zjnXnAcC55zrcx4InHOuz3kgcM65Pqegz7Y4JI0DP1rhy48E9qRYnKLox+Pux2OG/jzufjxmWP5xH29mY80eKFwg6ISk7Wa2pdfl6LZ+PO5+PGboz+Pux2OGdI/bU0POOdfnPBA451yf67dAcEWvC9Aj/Xjc/XjM0J/H3Y/HDCked1/1ETjnnFuq31oEzjnnGnggcM65Ptc3gUDSOZL+W9L9kt7R6/JkQdKxkr4iaaek70q6NNx+uKRbJP0g/H1Yr8uaNkklSd+WdEN4vx+Oeb2kayXdF/7Pz+iT435z+Pm+V9JnJQ2ttuOW9A+Sdku6N7at5TFKemd4bvtvSS9f7vv1RSCQVAI+BpwL/CzwGkk/29tSZaIG/IGZPQc4HbgkPM53ALea2YnAreH91eZSYGfsfj8c80eAL5rZs4GTCY5/VR+3pI3A7wNbzOy5QAm4iNV33J8GzmnY1vQYw+/4RcBJ4Ws+Hp7zEuuLQACcBtxvZg+Y2SxwDXBhj8uUOjN7zMzuDG9PEpwYNhIc65Xh064EXtmbEmZD0ibgFcAnY5tX+zGPAmcCnwIws1kz28sqP+5QGRiWVAbWEqxsuKqO28y+BjzVsLnVMV4IXGNmM2b2IMEaL6ct5/36JRBsBB6J3d8Vblu1JG0Gng/cARwdrf4W/j6qdyXLxIeBtwHzsW2r/ZifCYwD/ximxD4p6RBW+XGb2aPAB4CHgccIVjb8Eqv8uEOtjrHj81u/BAI12bZqx81KWgdcB7zJzCZ6XZ4sSTof2G1mO3pdli4rA6cCnzCz5wNPU/x0yEGFefELgROAZwCHSHptb0vVcx2f3/olEOwCjo3d30TQnFx1JFUIgsDVZva5cPMTkjaEj28AdveqfBl4MXCBpIcIUn5nS/oMq/uYIfhM7zKzO8L71xIEhtV+3C8DHjSzcTOrAp8DXsTqP25ofYwdn9/6JRB8CzhR0gmS1hB0rFzf4zKlTpIIcsY7zexDsYeuB34rvP1bwL92u2xZMbN3mtkmM9tM8H/9spm9llV8zABm9jjwiKSfCTe9FPgeq/y4CVJCp0taG37eX0rQF7bajxtaH+P1wEWSBiWdAJwI/Ney9mxmffEDnAd8H/gh8O5elyejY/x5gibhPcBd4c95wBEEowx+EP4+vNdlzej4zwJuCG+v+mMGTgG2h//vLwCH9clxvw+4D7gX+CdgcLUdN/BZgj6QKkGN/3faHSPw7vDc9t/Auct9P59iwjnn+ly/pIacc8614IHAOef6nAcC55zrcx4InHOuz3kgcM65PueBwPUtSXOS7or9pHZlrqTN8Zkjncuzcq8L4FwPTZnZKb0uhHO95i0C5xpIekjSX0v6r/DnWeH24yXdKume8Pdx4fajJX1e0t3hz4vCXZUk/X04d/6XJA2Hz/8pSV+UtEPS1yU9O9z+aUmXSfqmpAckvaonfwDXdzwQuH423JAaenXssQkzOw24nGB2U8LbV5nZ84CrgcvC7ZcBXzWzkwnm+/luuP1E4GNmdhKwF/jVcPsVwBvN7AXAW4GPx953A8EV4ucDf5XisTrXkl9Z7PqWpP1mtq7J9oeAs83sgXASv8fN7AhJe4ANZlYNtz9mZkdKGgc2mdlMbB+bgVssWEQESW8HKgRBZZxgKoDIoJk9R9Knw9dcHb5m0sxG0j9y5xbzPgLnmrMWt1s9p5mZ2O05YJigFb63Td9E/DXNphd2LnWeGnKuuVfHfv9nePubBDOcAvwG8I3w9q3AxVBfO3m01U4tWB/iQUm/Fj5fkk5OuezOLYsHAtfPGvsI4jn5QUl3EKyF/OZw2+8Dr5d0D/Cb4WOEv18i6TvADoK1Y9v5DeB3JN1N0J+w6pZNdcXifQTONQj7CLaY2Z5el8W5bvAWgXPO9TlvETjnXJ/zFoFzzvU5DwTOOdfnPBA451yf80DgnHN9zgOBc871uf8P1xvo6cJjHH8AAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(range(max_epochs), fehler)\n",
    "plt.xlabel('Epochen')\n",
    "plt.ylabel('Falsche Klassifikationen')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Als For-Schleife\n",
    "iter = 0\n",
    "fehler = True\n",
    "\n",
    "for iter in range(100):\n",
    "    for x, label in zip(feature, labels):\n",
    "        delta = label - perzeptron(w, x)\n",
    "        w += (delta * x)\n",
    "        if delta != 0:\n",
    "            fehler = False\n",
    "\n",
    "    if not fehler:\n",
    "        break\n",
    "else:\n",
    "    print(\"Es wurde keine Lösung gefunden.\")"
   ]
  },
  {
   "source": [
    "### Daten normalisieren"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "skalierungsfaktor = np.max(feature, 0)\n",
    "feature /= skalierungsfaktor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(5) # w zurücksetzen\n",
    "w = np.random.rand(feature.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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UNNg8XZYQQrjvjEer1fLCCy+QmppKVVUVmZmZjBs3juTk5CbLjR07lgULFrirDIEElwohvIvbGk90dDTR0dEABAcHM2jQIAoKCpo1HtG5JLhUCOFpnXK02b9/P5s3b2b06NHN3lu3bh0ZGRlcfvnlbNu2rcXPz5kzh8zMTDIzM33uqYCedDS4dPfhavaW1FBaY8Fqd3i6LCGEj1Nqa2tVd26gurqa8ePH8+ijj3LFFVc0ee/IkSNoNBqCgoJYunQpv//97zGZTCddX2Zm5mk/Ra+gsp7yGstpfbY7CTDqJLhUCB9zJsfOjubWMx6r1cqNN97IDTfc0KzpAISEhBAUFATAhAkTsFqtckbjBWobbBRU1LPDXM2Bsloq66w4VLf+fSKE6Ebcdo1HVVXuvvtuBg0axAMPPNDiMkVFRURGRqIoCrm5uTgcDiIiItxVkjhVElwqhHADtzWe1atX8+GHH5KSkkJGRgYAzz77LPn5+QBMmzaNhQsX8s4776DT6fDz8+ODDz6QA5qXOj64VKtRCPXXS3CpEOK0uP0aT0eTazzeRa/TEOqnp0eABJcK4c286RqPJBeIM2K1OSipbqCkWoJLhRDtI41HdBgJLhVCtIc0HuEWElwqhGiNNB7hXicElwb76Qj11xFs1MlEEiG6KWk8otOoqsqROitH6pwz44L9dPTw1xNolB9DIboT+Y0XHiHBpUJ0X9J4hMdJcKkQ3Ys0HuFVjgaXFlc14KfXNt6oqkMv07OF8BnSeITXqrfaqbfaMR+R4FIhfIk0HtEl1DbYnOGlSgPBjdOzg2V6thBdkjQe0bVIcKkQXZ40HtFlSXCpEF2TNB7hE+wOlbIaC2U1Fldwaai/Dj+Zni2E15HGI3yOBJcK4d2k8QifJsGlQngfaTyi25DgUiG8gzQe0f1IcKkQHiWNR3RrxweXHp2eLcGlQriX/HYJ0ej46dk6rcY1KUGCS4XoWNJ4hGiBTYJLhXAbaTxCtEGCS4XoWNJ4hDgFElwqxJmTxiPEaZLgUiFOjzQeIc6UBJcKcUqk8QjRgU4MLg1pvB4UaJBfNSGOkt8GIdzE7lApr7FQLsGlQjThtmk5Bw8eZMKECaSmppKWlsYbb7zRbBlVVXnkkUdISUkhPT2dTZs2uascITzqaHDpL8U17Cmuobi6AYvd4emyhPAIt53xaLVaXnjhBVJTU6mqqiIzM5Nx48aRnJzsWmbZsmXs2bMHk8lEbm4uM2bMYMWKFe4qSQivIMGlortz2096dHQ0qampAAQHBzNo0CAKCgqaLJOTk8OUKVNQFIX09HQqKyspLCx0V0lCeJ1ai53Cynp2mmvYV1pLea0Vh6p6uiwh3KpT/sTav38/mzdvZvTo0U2+XlBQQGxsrOt1TExMs+YkRLegqtQ02CioqGNHUTX55XUcqbeiShMSPsjtkwuqq6uZPHkyL774IiEhIU3ea+mXqqXpp3PmzGHu3LkAlJSUuKdQIbyEBJcKX+fWn2Sr1cqNN97IDTfcwBVXXNHs/ZiYGA4ePOh6fejQIaKjo5stN3XqVKZOnQpAZmam+woWwstIcKnwRW4balNVlbvvvptBgwbxwAMPtLjMxIkTmTdvHqqqsm7dOkJCQlpsPEKIY8Gle4tr2H24msNVDVhsMjNOdD1uO+NZvXo1H374ISkpKWRkZADw7LPPkp+fD8C0adOYMGECy5YtIyUlhYCAAGbPnu2ucoTwKRJcKroytzWesWPHUltbe9JlFEXh1VdfdVcJQnQLElwquhq5WimED5HgUtEVSOMRwhedEFwabHROSggySnCp8DxpPEL4OIdDpbLOSmWdBJcK79BtfvKO1FtZvbeMxJ4BcgFWdFtNgku1GtekBAkuFZ2p2zSer7YX8+QXOwg2ahnTL5zspHAGRwXJ2Lfotqx2Z3BpSXUDRr3WdY+QQf4wE27WbRrPb4ZFER5oYJHJzDc7i/lqRzERgQayEsPISoogIdxfxr5FtyXBpaIzdZvGY9BqOH9gT84f2JMai43vdpaQYzKTk3eYz7eYie3hR2ZiOFmJ4USH+nm6XCE8ptZid4aXHlEIPNqE/HQyPVt0mFYbT3p6epsf7tmzJ4sXL+7QgjpDoEHHZUOjuGxoFOW1FpZtKybHVMQnGwr4ZEMBA3oFkpUUztj+4YQF6D1drhCe0RhcWtNgo1BRCPbTEeqvI9iok9EBcUZabTx2u52FCxe2+kFVVbn22mvdUlRnCgswcMOoGG4YFUNBZT2L88wsMpl5d3U+76/JJ6VPCFmJ4WT06yGzgES3JcGloiMptbW1Leau//TTT4wdO/akH27PMh0tMzOTVatWuX07e4prWGQyk5NXxMHyevRahZFxoWQnRTAyLhSDTsa+hZDg0q6js46d7dFq4/FWnf3NU1WVLYeOsMhkZvFWM6U1VgIMWjISepCVGE5KnxAZ+xYCMOiOTc826qQJeRtvajxtnicvXryYP/3pT+Tn52Oz2VBVFUVRMJvNnVGfxymKwvDYUIbHhvLoxUms/bWCRXlFLN9WzHe7Sunhr+fs/mFkJ4YzoHegjH2LbkuCS0V7tXnGk5KSwkcffURKSopXHFS9pWvXW+2s2F1KTp6ZH3aVYLGrRIYYyeofTlZSOHFh/p4uUQivIMGl3sFbjp3QjjOe2NhYzjrrLK9oOt7ET69l/JDejB/Sm6p6G1/vcM6MW7i5kP/9XEhChD9ZieFkJobTK8jo6XKF8BgJLhUnarPxPPfcc1xxxRVkZ2djNB47gLb2cLfuKNhPx5UjorlyRDTF1Q0s3XqYHJOZ/6w7xH/WHSI5KoisxHDO7h9GiJ9Mzxbd1HHBpYrinBknwaXdU5uN59lnnyUwMJD6+nosFktn1NSl9QoycnNGHDdnxHGgrJZFeWa+3GLm7VUHmLs6n+ExIWQnhTOqbw+ZBSS6LVWV4NLurM1/5bKyMr788svOqMXnxIcHcPc5/bgrO4Ed5mpyTM57hF777leMOg2j+zpnxg2PDZELsKLbkuDS7qfNxjNu3Di+/vprLrzwws6oxycpikJyVDDJUcE8cmEiGw5UsMhkZum2w6z8pawxuNSZGZcswaWiG5Pg0u6hzVltvXv3pqamBqPRiF6v9/h0am+amXGmLHYHP/1SxqI8M1/vKKbe6iAi0EBmYhhZiRH0i5DgUiEA/A3HpmdLcOnp8aZjZ5tnPIcPH+6MOrolg1bDeQN7ct7AntRa7Hy3s5icPDOL8g7zxRYzMT38yJLgUiGos9ips9gpkuBSn9Bm4/n8888577zzCA0NBaCiooIVK1YwadIktxfXnQQYtEwcGsXEoVGU11pZvu1wK8GlYYQFGDxdrhCeIcGlPqHNobaMjAzWrl3b5GtjxoxhzZo1bi2sNd50utgZChuDS3PyzOwoqkajcCy4NKGHhDQKARJc2g7edOxs819IVZv3Jbvd7pZiRHPRoX5MzezL1My+ruDSRXlF/HPFPv61UmFkfCjZieGMjO+BUYJLRTflcKhU1FqpqLWi02pc9wgFGGRmnDdqs/Gkpqby2GOPMX36dBRF4c033yQ1NbUzahMnSOoVyIxx/Xng/H6YCqrIMRWxOM/Mun0V+Os1ZCSEkZUUzlAJLhXdmM3uoKzGQlmNBb1OQw8JLvU6bQ611dTU8Je//IVvv/0WgAsuuIDHHnuMwMDATinwRN50uugNbA4H6/Y5p2cv336Y6gY7of46xvYPl+BSIY7T3YNLvenYKY9F8CENNjs/7C5lkcnM90eDS4ONzplxElwqhEt3DC71pmNnq23/ueeea/PDJ1tm+vTp9O3bl1GjRrX4/ooVK4iKiiIjI4OMjAyef/75dpQrTsao0zI+uTevXTeUlb/L5vnLk0nsFcDCzYU8NH8rv1uwlYU/F1Jc1eDpUoXwqNoGGwUV9ewwV3OgrJaKOiuOFq5nC/do9RrPe++9R0hISKsfVFWV+fPn8+STT7b4/s0338xdd93FtGnTWl3H2LFjWbBgwSmUK9rrxODSZY3BpfNyDzEv9xCDo4LIluBS0d0dF1xaIMGlnabVxnP77bdTVVV10g/ffvvtrb6XlZXF/v37T78y0WF6BRm5KSOOmxqDSxfnHeZLU1GT4NKspHBGS3Cp6MYkuLTztPodnTlzpts3vm7dOjIyMoiKiuKFF15gyJAhbt9mdxcfHsBd5yQwPbsvO48Gl+aZeb0xuHRU3x5kS3Cp6OYkuNS9PNbKR4wYwY4dOwgKCmLp0qVcf/31mEymFpedM2cOc+fOBaCkpKQzy/RZiqIwOCqYwVHBPHxhIhsPVJJjMrN0m5lVv5QRZNRytgSXCiHBpW7g1llt+/fv5+qrr2b9+vVtLjt48GBWrlxJz549T7qcN83M8EXHB5d+s6OYOquDiEC9c3p2Ujj9IgJk7FsIul5wqTcdOz12xlNUVERkZCSKopCbm4vD4SAiIsJT5YhGzYJLd5U4b1TdepgvTRJcKsRRElx6+tpsPLt372bGjBkcPnyY9evXYzKZWLRoEY8//vhJP3frrbeyYsUKSktLSUpK4sknn8RqtQIwbdo0Fi5cyDvvvINOp8PPz48PPvhA/pL2MgEGLRNTIpmYEkl5rZWvtjtnxh0NLk3qFUhWYjiZiRJcKroxCS49ZW0OtY0fP54///nP3H///a5g0FGjRrVr+MwdvOl0sbsqrKxnyVZnevb2xuDSsxqDS8dIcKkQgPcFl3rTsbPN70ZtbS2jR49u8jWtVmZ2dGfRoX7cMTaeO8bG80txDYvyzOSYinhzxT7eluBSIQAJLj2ZNhtPREQEe/fudZ0yLly4kKioKLcXJrqGxF6BPHB+f+4/r+Xg0vSEMLITwxkaI8GlovuS4NKm2mw8r776Kvfddx+7du0iMTGRhIQE19RmIY5SFIVhMSEMiwnhsfEDWLuv3BVc+sPuUmdwaT9nZtxACS4V3ZjV5qC4qoHiqoZuG1za7unUNTU1OBwOgoOD3V3TSXnTOKVoW4PNzordZSzKK+K7nceCSzMTw8hOjCAuXIJLhQD3B5d607GzzTOe119/vdnXQkJCSE1NZfjw4W4pSvgOo07LRcm9uCi5F1X1Nr7ZUUyOqYjPNhex4Oci+ob7u6Zn9wo2erpcITymtsHmDC9VGggyHpue7Ys3b7fZeDZu3MjGjRu59NJLAVi6dClpaWm88847XHXVVTz88MNuL1L4hmA/HVeMiOaKEdGUVFtYutXcLLg0KzGcs/uFEeovwaWim1JVquttVPtwcGmbQ22TJk3iww8/JCgoCIDq6mpuvPFGPvnkEzIzM9m4cWOnFHqUN50uio6RX17HIpOZL01F7C2pRaPA8NgQshMjnMGlMgtICAKMOvpFBJz2573p2NnmGU9+fj4Gw7GbA/V6Pfn5+fj7+zf5uhCnKy7Mv+Xg0u+dwaVp8aFkJ0UwQoJLRTem+tDzgtpsPNdddx3nnnsul112GQCLFy/m2muvpaamhuTkZLcXKLqPE4NLN+U3BpduNfPT3nKCjFrG9AsjKzGcIdHBPjn2LUR30K5ZbRs2bGDNmjWoqsrZZ59NWlpaZ9TWIm86XRSdw2p38NPeMhaZzHwtwaWim/I3aOnfM/C0P+9Nx8525TiMGDGC6Oho7HY74Bx+i4uLc2thQhyl12o4d0BPzh1wLLh0kckswaVCdFFtNp4333yT559/nt69e6PValFVFUVRWLduXWfUJ0QTrQWX/ve44NLMxDAy+4cTHijXIIXwRm02njfeeIOff/5ZHlkgvE5YgJ7r0mK4Li2GoiP1LMk7TE5eEe+vOcgHaw5yVp9gshPDyegXRpAXhDQKIZza/G2MjY0lNDS0M2oR4rRFhfhx+9h4bh8bz96SGhaZzOTkmXnzx/28veoAI+NCyUoKJ02CS4XwuDYbT0JCAhdffDETJkzAaDx2Z/kDDzzg1sKEOF39ewZy//n9ue+8fuQVVJFjMrN4q5l1+yW4VAhv0GbjiYuLIy4uDovFgsVi6YyahOgQiqIwNCaEoTEhPDo+iXX7ysk5PrjUT8fY/hJcKkRna3dIqLfwpimBoms6Prj0+12lNNgc9A42kJkYTnZiBPESXCq8ULeaTl1cXMzf//53tm3bRkNDg+vrS5YscWthQoHxS5UAACAASURBVLjL8cGl1Q02vt5RzCKTmc83F7HwuODSzMRwektwqRAdrtWrrFdddRUAU6dOZeDAgezfv58//OEPxMfHe/QGUiE6UpBRxxXDo3n7phH88HAWT14ykLAAPfNyD3HPxyae/GIHS7cdprLO6ulShfAZrQ61HTlyhJCQEMaOHctPP/1Eenq6696d8ePHs3z58k4t9ChvOl0Uviu/vI7FeWa+3FLELxJcKrxAtxhqu/LKK/nmm2/Q653x9FFRUSxZsoTo6GgOHTrUaQUK4QlxYf5Mz07gzqy+7DpcQ46piEWmpsGlWYnhpMaFSnCpEKeo1cbzzTffAPD73/+eyspKXnjhBR555BGOHDnCiy++2GkFCuFJiqIwKDKIQZFJPHSBM7h0kcnM0m2H+WlvOYEGZ3BpdlI4yVHBMj1biHZoc3LB0VTq0NBQli5dCsCsWbPcW5UQXkijKKTF9yAtvgdPTBjA6r3l5OQV8fX2Yr7ZWUJ4gJ7Mxsy4/j0luFSI1pzWdOqBAweya9cud9TTJm8apxQCoM5q57udJSzKM7Nidyk2h0qf0GPBpX16SHCpOHPd4hrPyfjSA4mEOFP+ei2XpkRyaUokFXVWlm87zCKTmU83FvDfjQUk9gpwTs+W4FIhgNNsPDKEIETLevi3HVyalRjOGAkuFd1Yqz/5vXv3brHBqKpKXV1dmyuePn06S5cupVevXqxfv77F9fzud79j2bJlBAQE8NZbb5GamnqK5QvhvY4PLv21pIZFeWa+NJmZ/eN+3ll1gNS4ULITw0nrG4pRJ9OzO53jAFrLYhS1GId2CA79xaAEebqqbqHVxnP48OEzWvHNN9/MXXfdxbRp01p8f9myZezZsweTyURubi4zZsxgxYoVZ7RNIbxVv56B3Hdef+49tx9bCxuDS/PM5DYGl47u65wZN8xXg0vtO9A1vAeOfFCCcOh/g0M/ERQF1AagDgh1vu4Eim0j2vrXACsKKhr7bjTWr7AF/AWUHp1SQ3fmtnP9rKws9u/f3+r7OTk5TJkyBUVRSE9Pp7KyksLCQqKjo91VkhAepygKKX1CSOkTwu8vSiJ3vzO4dNm2w6zY4wwuPbt/GFlJEQzyleBS+y/o6l5AoTFkWD2CxvIpqBUoahWKbTWggOKP3XAHqj7dvfWoKtr614/VAyhYUdUqNJaFOIy3u3f7wn2Npy0FBQXExsa6XsfExFBQUCCNR3QbWo3CmH7hjOkXzh8vHcSKPaUsMpn5blcJS7cV+0xwqdbyP6Bp5JCCBY11CaBFOfqeakHb8AY2TShoB7mtHsX2PdDQ/Os40Ng2SOPpBB5rPC3NjGvtr7s5c+Ywd+5cAEpKStxalxCeYNBpuHBwLy4c3DS49IvG4NL4xuDSrC4YXKo4DqDQ0kxYBwqOE75mQWv5HLv/o26rR2NbRavnkYrMOuwMHms8MTExHDx40PX60KFDrZ7tTJ06lalTpwLOuehC+LKjwaVXDI+mtMbC0q2HyTEV8WHuIT7MPcSgyCCyEsMZ2z+MUH/96W9ILQG1CpRYUNpYj307Ossn4CgATTQ2w/WgHdK+zWhiwV7a+sH+OAqAam7XelHL0da/h2LfCCiounTsxtvanCCg0HLgqwo4tBnt27Y4Ix5rPBMnTmT27Nlce+215ObmEhISIsNsQpwgItDAlPRYpqTHcvBocKnJzJyfDvDu6gMMi3XOjEs/leBStQpd/ctg/xXQAip2402o+gtaXt62GV39K8euidir0NX9FbvfQ6i6EW1uzm64Bl1dHmA/VkJrpaFB1Qxsxz5Y0dU+6bxOdPSsybYGnWMvNv+XQGk9P8+uG4PW/msLDUiLw3Bl29sWZ8xtjefWW29lxYoVlJaWkpSUxJNPPonV6vyHnjZtGhMmTGDZsmWkpKQQEBDA7Nmz3VWKED4hNsyfO7MTuDM7gV3manJMZnJMRfzj+18xaBVG9e3RruBSXf1LYP8F5bhGoG34AJumD2iTmy9v+U+TC/HgvEajtfwbW1uNR62npcOMgrP5qOhdDUBFAYzYDVecfJ2AYvsR1OomQ3UKdlRHOYp9M6qu9VszVN0FYP0B1VGEQkPjdvXYjdNlqK2TyBNIhejCHKrKz/mV5DQGl5bXWl3BpVmJ4QyJPiG4VD2Mrub3zRqJCqjawdj9HgF0KLbVKI79qJo4tA1zWrxGo6JgC/yQFsfQ1Fq09W81DoM5cF7POfHzAMGgCQH1CKpmMHbjDaDp0/oOq/WN613XwvUhUNHiMF6PQ/+b1tcBoNpQbD+hsW1A1fTAob8QNHEn/4yHdfvIHCGEd9AoCiPjezDyuODSRXlmvt7hDC4NC9CT2T+c7KTG4FJHJc5f+xPPYAD7LnQ1d+N8PqTSeDZwkokMSnDTpnN0wpCiNJ5V7UbB1vrHAZVqbP6vg9K+PDtt/SwU+5YWm46THlWJaXtFig5Vfw52/Tnt2q7oWNJ4hM9rqLOg0SjojWdwIb4L0Gs1nDMggnMGRFBntfP9rhLXIxxy8sxEhxrJ6h/CeX2CiA2pbfZ5pfHMROVYPzk6FKWiaXKwVzFgN1ze+KISbf1cFHtjQol2ANj3nrTpHL/VdlMrGptO65MDQI+qbfu6k/AsaTzCZxXsKeL1u95hZ+4eQGH4uLN44J+/JTza9+9M99drueSsSC45yxlc+tX2YhaZipi/qZhPN91MUriZc/puJyt+JxEB1U0+e2IrUFAbr8f44Rw20+AwTELVXQqqA13dM+AodjUm1b6b1qcPHKMCaJPafbaDWo7zkNVy43GeQTWg2L5xvlJ6OJuQ0sJhTrU59+X4azqqA8W2Eq3tO8CBXZeNqjuv5c+fKbUexb4F1AZU3TBQQjt+G15MrvEIn1R7pI5pZz1CdXmN654xjU5Dr9gIZm9+EW03zUYzH2lgyVYzOVt+YWuR81wmJTKfc/ruYGzcLoIMzW+sBFDRYQt8F6gCgl0HY8W2qTEFoP6E5Zs3sMZ/BRQcqBhAMWLzfxY07ZzNqtajq5ne7PpU81qdkwVAC4oem/8fQdM4/KZWoq3/F4p9s7MibSI243TQxKCtexXF/jNK482lKgZU7SDsfn84pROzNtnz0NW9zLEpFnbsxsmo+ktOslP1BGj3079XGKom8bSihbzp2ClnPMInLX77axrqLE1uVHbYHBwprWLj1yZGT+iewzGRIUZuOzue2852Bpcu2fg2X26P5o1143lr/QWkRf/KOQnbGd1nL0adc6hMRYeqy2hsNmFN1qeohdDCkJrzkHpseE5FA/hjN05BcRShaqJQdWe3/2wHQPHDYbgCjeVzV3NocTFUjl7DUtV6dPUvY/N/xbkndX8Ch9k1o0+170FX+zQ2vwdR7JtOiNGxgH03OPJAm9L+Ok9GrUdX93KzRq1t+Bibdgho+jbfH+tXaBv+g07vQF9dhKqJxRo4q/0N2wtJ4xE+5cC2g7x46xvk7yxAtTc/mbdZbBT+0voNig6Hg4M7CzH4G4hK6OXOUj2uX89A7r3gYmak347pcC++3JHKl7tSWbsqCX+dhYzYvWT33c3wPnVgvK3FdTiUWDToOLH5qPjh0KWjse8E6lC1Q50z1pSe7RiEa53DcCWqEoXW+jm0mohwjIKK6igFtRDUUnCUNZlG7hxGtKK1LoUWJyzUo7FtxdFBjcd5ptXS2YoNjfUHHMZbmn7ZvhNtg3M6u/O/enDsRV9zD9agBZ0WqtrRpPEIn1FXXc/j4/9MTUUNrT2rUKvT0m9ofIvvbf5uKy/fMZv6mnrsVjsRseHM/HgGfc/y7mm2Z0LVJmML/ogUw9sMjd7Co+fXsMZ8E4u2VrF0+0C+3zeYED8dZ/cvJTtRZWBkEJrjD3baoaDpjeoocE0mUNGAEoDD+FscbSUinE7N+rOx6c9GsX6LtuF911lKS8N7ThrAiuIooqXmomBBpQ5aaKBgcE737rDiLbR8/cuBojY/i9NaltI8584BjkIUxy5UN2bauZM0HuH1dqzbQ86by6k4fIT0S1MZf9t5+AU2n+b708JcbFZbq01Hb9QRN7gPKdmDm71XnF/Cc9f9nYbaY0MtRXsPc3/GTO76+61cOq2Vu/p9gKrthy3gedfrjGDISIInJzr4cU8pOXlmvttZwrJtxfQKMjgz45LC6RseAIqCzf+PaBvmgW014EDVjsLud3PbMTxnWrd+HDZNb3SWL8FRCGoJauMVniYUAyhxqBoLLbUmFSMOXQZa+74WtqLg0HVcTJeqGwoN9hbeMWLXNU/lVqho5axOC2pFh9XV2aTxiJOyWmys+WI9eT/uoGdcBBdMye7UWWHL5n7HO4/Ow1JvRVVVdqzdw5J3vuWVH5/FP6jp9YHSwvImjeN4Bn8DF99xHjf98ZoWw2iXv/s9dlsLQy0qzHlsHglnxTJkbNf86/J0GXQaLhjciwsG96KmMbg0x2Tmiy1FLNx8YnDpncCdnV+kNgWbfwq6uqdR7MXN3lZRsPnd7xyS0iSBNgHVvve4tAQNKIGounOxaRLR1b8CauNUc8WAze9BUDrwjEfpgd14A9qGTwBb40QLo3P2nXZ4s8Ud2jQ09r0tTKiworYzK88bSeMRraqrruexC/6Pol8PU1/TgN6o59MXv+CZz37XKQfh+poG3nlsHg11x37pLHUWivNLWDrnO66c0XQW0IC0/hj9DdTXNB2y8As08sSHD5B64dBWt3U4vxSbpeX7TqwNNr54Y7lPNJ7ig6XUVNQSOyganb79v/6BRh2XD4/mcncHl56uFs9WAFTQNJ7hKmDzewKN5VM0thWg2lF1adiNN4JidM5wC5gF6n5QHaBJOGnm2+lS9Zdi0w5BY/0BRW3ArstwNp0Wxgkd+gvR2L5GdZQDlsZzHz/sxnudN/B2UdJ4RKs+e30JBXuKsNQ7/zq0NlixNsBLt73JnJ1/d/tDyvZs/LXFac+WOiurv8ht0niOlFSxd/M+DP4GLA1WHI1nLwY/PX3PimX4uLNOuq0R56ewcsE6rPUt3yNSVlR+BnvieeXmSv4y5XV+2bQPrU6LRqfhntduI/uaMae8ruODSw9VNAaXbjkuuDQmhKzEcNITwghob3DpmVKCQC1r4Q0jziDUo8sZcRhvwmG8qZX1KKAkdHh5zWgScBjbsR3FD5v/C2isX+FQVuPQJWM3TnHOMuzCpPGIVq347xpX0zleVXk1BXuKiBng3umcgT0CcNhbjkYJiTj2196+vHwev+g5bFY7ljoLOr0WtBp6xoRx0W3nccUDl6DRnPwv16yr0/n0b19wcFdhs/cMfnrSL2k9dLIrePbKl9i3Nb+xITv/TV+/6x2iEyNJSu13yuuz2+wUHyyjR1gg07ISmJblDC5dlOcMLp31wz4MK/eTFt+D7KS2g0vPlN1wGdqGj5sMSakYcOgv7rIzv1wUfxyGSdgMV2ILPP2sNm8ijUe0Sm9s+cdDdaidEj+TkBJHz9gICnYX4nAcu8BqDDDwm7vHu16/fvc71B6pc722We1odRpSspO5/rHL27UtvVHPSyue4a83zeLnb/Jc9//ojTp6RIZyiRdNLti/9SBL535HZXEloy9JJevqDPSG1n+V9+Xlc2h3oess8ChLg5Uv31jOQ+9MP6Xtf//JT/zrkX9jbbDisDtIvzSVB2ZPY2BkEAMjg3hwXH9+PniEHFMRS7YeZvWv5ScPLu0Aqu4SHI4KNNalOM9wbDh02TgM13XodkTHkMYjWnXx1PN5b+bHTS7YK4pCn6Qoesf3dPv2FUXhjwse4elJL1JeVIFGq8FmsTH5D1cy/Hzn0Fl9TQN7t+xv9lm7zcHanI2ntL2AYH+e/fz3/PxtHl+8sZzK4krSJ45k4vQLCQwN6JB9OlPffbyKN+57F5vFhsPuIHfpZha+toTh5yVTfLCMYeckc/6NWU0mXpQXVbQ4ZKk6VA7nn9oTffN+3MEb981t8jOxbvHPvDJ1NjM/eRBw/rulxoWSGhfK4xcPYM2v5eSYmgeXZiWFk9gzoGOGbBVwGCc7n6ejFoMSDopvnB34Imk8olUTpo7D9MN21i/bjIIzcsYvwMjj8+7vtBqiEnoxe/OL7Nn0K1Wl1QwcnUhQj2MHFI1WafXA1doZW1tGjEthxLgOulO9AzXUNvDmA+9hOW6yRUNNA/tMB9i/NR/VobJh2WYWvLqYV3581jUcmZiagLWFiRMGPz1pFw07pRrmv5zTbOagtcHKxq9MlJsrCYtsmjmm12rIToogO+kkwaWJ4WQlRhDT4xRSDFqj+IHiu/dd+QppPKJVWq2Gx+fdz6+mA+xct4fw6DBGXjT0lGZDdQRFURgwsn+L7xn8DKRdNIwNy7dgtx27P0Lvp+fCW87trBI7xc7cX9C0cp1EbRyKbKi1UFZYwfyXcrjjhcmA83rYlTMu5fN/LKWh1jnjT2fQERwRxISp406phsP7m09Zdq5PS3lRRbPGc7zjg0sr66wsPxpcurGQTzcW0r9nAFmJ4WQmhhMRKA9k82XSeESb+g2Nb/Vuf29w/z+n8sTFz1NyqAzV4byOMWh0Ejc80b7rO12FX6CxybWu1tgsNn76PNfVeACmPHUV/YfF8/mspVSVVpM+MZWrHpxIUNipDUedlT2Ywr3mZvc8OewO+iRFtXs9of56rh3Zh2tH9jkWXJpn5oO1B/n32oMMiQ4mKymcMQlhBPvJYcrXSDq18AmqqpL34w6Kfj1Mv2HxRESHsfErE3o/PaMmDCcg2N/TJZ4xh8PBtCGPUJxf2uay8UNimZX7fJvLnarDB0p4IGMmddX1rrMsY4CRG564nKsfvuyM1/9raS2LGx/pva+sDp1GYURcKNmJ4YzqG4qxm6aKg289gVQaj/A5n89axgd//C9anQZFUVBVlcfn3c/IU7ye4Y0ObDvIkxP/SkOdc8isvrrB+bSc436LjQEG7njhRi757akNo7VX0a+H+fC5BZhWbKdHZCjXPHIZmVc2j3s5E6qqsq2wihyTmUVbzRRXWfDTaUhP6EFWUgTDYoLRtTFF3tdI4/Egb/rmCe/zq+kAvz//T00uwIPzYPz+3n/4xJmP3WZnyw/bqS6vJjopipdu/SflRc7cLpvNTvZV6Tzw1rQ2713qKuwOlfX7K8gxFbFsezFV9bbG4FLn9OxBJwaX+ihfajwyeCp8yncfrWox+kaj0bB+6WbOufbU79T3NlqdltQLjs26e/Pnv7J99S5KDpUzIK0f0f0jPVhdx9NqFDL6hZHRL4ynLh3Ej3tKWZRn5tvjgkszGzPj+ob7uz1RQ5w5aTzCpzTUWVpMO1BVtdlZkK9QFMUncuTa48Tg0m92lpBjKuLLLUV8trmIuLBjwaWRIc0TzIV3kMYjfMrYy0fz3byVzYJC7TaHT1zjEccEGnVMGhbFpGFRlNVYWLrtMItMZj5af4iP1h9iUGQgWYkRngsuFa2SxiN8yrBzkxkzKY01X2ygvqYBjVaDzqDj5meu6dTHOYjOFR5o4MbRsdw4+rjgUpOHg0tFq2RygfA5qqqy5Yft/LRwHYYAA+NuzPLq+5CE+xwfXFpQ2YBBqzAyvgfZic7gUoOu60zAkMkFQngxRVEYft4Qhp/XdR+UJTrGicGli0xmFm81s6YxuDQjIYyspHDOckNwqWidW9v98uXLGT58OCkpKbz00kvN3l+xYgVRUVFkZGSQkZHB8893/A1vQghxNLj0yUsHsuKRTN6eMpwLk3uxZl85f1q8i7s+2sK7qw+wp7jGlUwu3MdtZzx2u52HHnqInJwcYmJiyM7OZuLEiSQnJzdZbuzYsSxYsMBdZQghRBM6jYaspAiykiJ4ZqKd73eVkpNX5MyOyztMdIiRrCTnzLiYHl3/vi9v5LbGs379ehITE+nXz/mQqWuuuYacnJxmjUcIITzFT69lwlm9mXBWbyrrrHy1vZicloJL+4cTESTBpR3FbY2noKCAmJgY1+uYmBhyc3ObLbdu3ToyMjKIiorihRdeYMgQGZcXQnS+UH8914zswzUj+3C4qoHFeRJc6i5u++61NE564h3FI0aMYMeOHQQFBbF06VKuv/56TCZTs8/NmTOHuXPnAlBScmoPrhJCiFPVO9jIbWfHc9vZ8ewrrXXOjNtSxFs/7mfOqgOMiA0hKymCUfGh+OllevapclvjiYmJ4dChQ67Xhw4dIjo6uskyISEhrv+fMGECDz74ICUlJfTs2fTpllOnTmXq1KmAc0qgEEJ0loSIAO49tx/3nJPAtqJqFpmKyMkzs/7bvfjpNIxOcE7PHhYb0u2CS0+X2xpPWloae/bsYd++ffTp04f58+fz7rvvNlmmqKiIyMhIFEUhNzcXh8NBRESEu0oSQojTpigKZ0UHc1Z0MI9cmMSGAxXkND5N9cc9ZQT76Rjbzzk9u7sEl54utzUenU7HK6+8wqRJk7Db7dxyyy0MGTKEt99+G4Bp06axcOFC3nnnHXQ6HX5+fnzwwQcS8CeE8HpajUJ6QhjpCWE8eclAVv5SSo6pMbh0uwSXtkWSC4QQooMcDS5dlGdm1Z5S7CodFlwqyQVCCCGaOTG4dNm2w+QcF1w6sHcgWYnhjO0fTo+A7htcKo1HCCHcIDzQwOTRsUx2BZceJsdUxNzV+by3Jp+hMSFkd9PgUmk8QgjhZjE9/JmW1ZdpWX3ZdbiaxSZnevasH/ahX7mftC4aXHq6pPEIIUQnGtg7iIEXBDGjGweXSuMRQggPOBpcmhoXyuMTklj7azk5JjNfbS/m210lhAXoGds/jKzEcJJ6nf6kAm8kjUcIITxMp9GQmRhBZmIET0+088Nu5/Tso8GlUSFGzhvYk1vGxNEvIsDT5Z4xaTxCCOFF/PRaLh7Sm4uH9OZI/dHgUjOfrD9EgEHL7y9K8nSJZ0wajxBCeKkQPz1Xp/bh6lRncKmvkMYj3K7i8BF+nL+GqrJqRow7i+SzB8qd3EKcot7Bp3/zqbeRxiPcatM3eTx/w6uoDhVLg5XPXl/CiAtSeOw/96PV+v60USFEc/KbL9zGarHx15tn0VBrwVJvBRXqaxrY9E0eK/+31tPlCSE8RBpPG8rNlWxbvYvyogpPl9Ll7Fy7B9XRPAqwoaaBb+et9EBFQghvIENtrbDb7Pzj3rn8+OlqdAYdDbUWdAYdWp2G4eefxW3P3UCfxEhPl+nVFI0CtJxBq/jojXFCiLbJGU8rPn7hM1b+by3WBht1VfU47A4sdRbqqupZm7ORR7KfpqxQzoJOZlB6Ilpd8wwqv0AjF958jgcqEkJ4g27VeAr3mvns9SV88cYyivNP/gjtRbO/xlJnafE91aHSUGfhyzeXu6NMn6HT6/jDxzPwCzTiF2hEq9diDDAw5jdpjL1ilKfLE0J4SLcZavvfKzl89OeFOBwOVAfMeeJDzr32bKa/cguBoc3vBK6tqjvp+mwWG9tX73JXuT4jJWswc3e9yk+f5VJVVsOI888iMTXB02UJITyoWzSe/B2H+Oj5hc6ZVcf5/uOfWLd4Ey8sn0m/ofFN3huQ1o+d63456XodDkeH1FddXsOG5ZtRVUgbP4zg8KAOWa+3COoRyPjbzvN0GUIIL9EthtpWfZaLzWJv8b3aI3W8dPubzb4+/ZVbMAYYT3oRfGfuL+zesPeMalv5v7XclvQA/3zgPd6c8R63D5jBdx97x1MChRDCHbpF41HVkz/du2jvYUoLypp8LSm1H6/+9CdGjEuBVnqPald5bfrbp11XWWEFr975Lyz1Vuqq66mrrsdSb+WNe+dSfLD0tNcrhBDerFs0nrGTRqMznGxUUUVRmn8rYgZE88cFj9AzJqLVTx7aU0R1ec1p1fXT57ktV6OqrFqw7rTWKYQQ3q5bNJ6+Z8VyzSOXoWkhokVRFGIH9SE8ukeLn9VqNfzlq5no9C0/mlaBNppa6yx1Fuz25teJ7DZHs+tRQgjhK7pF4wG44YkreOXHZ+gd3xOdQYtWp8E/yI/giCB+//69J/1s7/ieXP/EFej99E2+rtVrSb1wKH6BzcP7HA4Hm7/fxvL3vueXTftaXO/oS0agbeExtzqDjtETRrR/54QQogvpFrPajuo/PIG3t73M1pU72bX+FyJiwjl7UhoGP0Obn7364Yns2fgrm77JQ6vVoKoqUf16M2P2tGbLlpsreWL8nykvqnDNfEseM5Cn5j+E3nisecUNjuE3d48nZ/ZXWOqcZzgGfz0X334+/YbFN1uvEEL4AqW2tvbkV969TGZmJqtWeW7WV/6OQ+zdcoDIvj0ZlJ7UYrz/M1f8jc3fbcVuOzaMZvDTc+VDlzLlyaubLb9j3R6+/3gVqgrnXnc2Q84eeEY1qqrK5u+2sjZnI35BRs6fnEV8cswZrVMI0bV5+th5PGk8Hay+poEbY+7CZm0+fTs8ugfv7XndrdtXVZW/3fpP1i/9mfqaBjQ6DTqdlt++OIUJU8e5ddtCCO/lTcfObnONp7PYrLZWYjGdaQfutnH5FlfTAXA0TlR459F5HCmtcvv2hRCiLdJ4OlhQj0DiBzcf1tLqtWRclub27a9csNbVdJpsX6dl0zd5bt++EEK0xa2NZ/ny5QwfPpyUlBReeumlZu+rqsojjzxCSkoK6enpbNq0yZ3ldJoH/3UnASH+GBpnwfkFGgmL7MHNz1zr9m3r/Qwtpy0ouOoRQghPctusNrvdzkMPPUROTg4xMTFkZ2czceJEkpOTXcssW7aMPXv2YDKZyM3NZcaMGaxYscJdJXWafsPieWvL3/j63ys4tKuIQRmJnHf9WIwB7n9m+oU3Z/PdvJU0nJCsraow8sKhbt++EEK0xW2NZ/369SQmJtKvXz8ArrnmGnJycpo0npycHKZMmYKiKKSnp1NZWUlhYSHR0dHuKqvThPYK4eqHL+v07Q4clci1j07ik798jkanQaMoqKrKzI9ndErjE0KItrit8RQUFBATc+xaR0xMDLm5uc2WiY2NbbJMQUFBs8YzTw0fzgAACXJJREFUZ84c5s6dC0BJycmfoyPgukcnMW5KFhu/NmH0NzD6khEEBPt7uiwhhADc2HhaCuY88Z6X9iwDMHXqVKZOnQo4pwSKtvWMCWf8red6ugwhhGjGbZMLYmJiOHTokOv1oUOHmp3JxMTEcPDgwZMuI4QQwre4rfGkpaWxZ88e9u3bh8ViYf78+UycOLHJMhMnTmTevHmoqsq6desICQmRxiOEED7ObUNtOp2OV155hUmTJmG327nlllsYMmQIb7/tfH7NtGnTmDBhAsuWLSMlJYWAgABmz57trnKEEEJ4CYnMEUKIbsCbjp2SXCCEEKJTdbnHIhw4cIBBgwbRs2dPT5fiMSUlJbL/3XT/u/O+g+z/mez/gQMHOria09flhtrAu04ZPUH2v/vuf3fed5D995X9l6E2IYQQnUoajxBCiE6lnTlz5jOeLuJ0jBw50tMleJTsf/fd/+687yD77wv73yWv8QghhOi6ZKhNCCFEp/LqxtNdHyR3VFv7//HHH5Oenk56ejrnn38+W7Zs8UCV7tHWvh+1fv16goKCWLhwYSdW537t2f8VK1aQkZFBWloa48eP7+QK3aut/a+srOTqq6927f8HH3zggSrdY/r06fTt25dRo0a1+L4vHPe8tvEcfZDcZ599xsaNG/n000/Zvn17k2WOf5DcrFmzmDFjhoeq7Xjt2f+EhASWLVvGunXrePzxx7nvvvs8VG3Has++H13uqaee4sILL/RAle7Tnv2vqKjgwQcfZP78+WzYsIH//Oc/Hqq247Vn/9966y2Sk5NZu3YtS5cu5YknnsBisbSyxq7l5ptv5rPPPmv1fV847nlt4zn+QXIGg8H1ILnjtfYgOV/Qnv0fM2YMYWFhAKSnpzdJA+/K2rPvAG+++SaXX345vXv39kCV7tOe/f/kk0+YNGkScXFxAD71PWjP/iuKQlVVFaqqUlNTQ1hYGDpdl7sfvkVZWVmEh4e3+r4vHPe8tvG09CC5goKCZsu09CA5X9Ce/T/e+++/7zPDLe3Z90OHDvHFF18wbdq0zi7P7dqz/3v27KGiooKLL76YsWPHMm/evM4u023as/933XUXO3fupH///owePZq//e1vaDReezjrUL5w3PPaPxE68kFyXdGp7NsPP/zA+++/z9dff+3usjpFe/b90Ucf5bnnnkOr1XZWWZ2mPftvs9nYtGkTixcvpq6ujvPPP5/09HQGDBjQWWW6TXv2/+uvv2bYsGEsWbKEvXv3ctlll5GZmUlISEhnlekxvnDc89rG090fJNee/QcwmUzcc889fPbZZ0RERHRmiW7Tnn3fuHEjt9xyCwClpaUsW7YMrVbLpEmTOrVWd2jvz35ERASBgYEEBgaSmZmJyWTyicbTnv3/4IMP+N3vfoeiKCQmJpKQkMDOnTsZPXp0Z5fb6XzhuOe156bd/UFy7dn//Px8Jk+ezJw5c3zigHNUe/Z9+/bt7Nixgx07dnDllVfy6quv+kTTgfbt/2WXXcZPP/2EzWaj9v/bu5fQJrY4juNfYyyWaqn4QPABimi10khjJ0YiDsGahVQFQcUIiVKMLgoVhQo+QAgFUWhQQagPFKFWXSkERQrVRAyjUgQXPkqz6KJSqEGpxEcKvQu5Ablae6+5U2/u77OaTA4z/zOL+XPOzPxPNsvTp09ZsmTJOEVcWGPp/7x58+jq6gJgYGCA169fs2DBgvEI13bFcN/7bUc8//eF5MbS/5aWFjKZTP6tFqfTWRQFBMfS92I2lv5XVlZSV1eHYRg4HA7C4TBVVVXjHHlhjKX/hw4dIhKJUFtby8jICNFotGiqVodCIRKJBG/fvmXRokUcOXKEXC4HFM99T5ULRETEVr/tVJuIiBQnJR4REbGVEo+IiNhKiUdERGylxCMiIrZS4hEREVsp8UhRGhgYIBwOs2zZMlavXo1pmty6deuH7Xfs2EF/fz+bN2/m3bt3/0pMgUAAl8v13YKnf9eZM2dYvHgx+/fvL0BkIvb6bT8gFfmnRkZG2LZtG8FgkMuXLwPQ19dHPB7/S9vh4WGcTift7e0Ao5ajL4RLly7hdrt/+TiNjY1UVFTQ3d1dgKhE7KURjxSd+/fvU1JS8k2Fg/nz57Nv3z4Arl69SjAYZMuWLdTX15PJZNi6dSuGYbB27VqeP38OQDKZxOPx4PF4WLVqFUNDQ7x584a6ujo8Hg8rV67MV4ro7OzENE28Xi/BYJAPHz78NM7e3l42bNiAx+PB6/WSTqdJJBKsX7+enTt3Ul1dzdGjR+no6GDNmjXU1taSTqf/hSsmYi8lHik6L168YMWKFaO2sSyL8+fPc+fOHaLRKC6Xi8ePH3P8+HEaGhoAiMVixGIxLMuis7OT0tJSbty4wbp167AsC8uyqK6uZnBwkBMnThCPx0mlUtTU1HD69Omfxrlr1y727NmDZVl0dXUxe/Zs4Gvh15MnT/LkyRPa29vp6ekhmUwSDoc5d+7cr18gkXGmqTYpek1NTaRSKSZNmsTDhw8B8Pv9+cW2Hj16xLVr1wAwTZNMJsP79+/xer00Nzezfft2Nm7cyNy5c3G73ezdu5dcLkd9fT0ul4tkMsnLly/x+/0A5HI5DMMYNaahoSH6+/vZtGkTAJMnT87/53a780UfFy5cmF9htaqqigcPHhTwyoiMDyUeKTpLly795llNLBZjcHAQn8+X31dWVpbf/tH6JgcPHswXZDRNk3g8js/n4969e9y9e5eGhgaampqoqKjA7/dz5cqVMcf4vXP+qaSkJL/tcDjyvx0OB8PDw2M+h8jvSlNtUnRM0+TTp0+0tbXl9338+PGH7X0+Hx0dHQAkEgmmT59OeXk56XSa5cuXc+DAAWpqanj16hV9fX3MmjWL3bt3EwqFePbsGYZhkEql6O3tBSCbzdLT0zNqjOXl5cyZM4fbt28D8PnzZ7LZ7K92XeQ/QSMeKToTJkzg+vXrNDc309rayowZMygrKyMajX63/eHDh4lEIhiGQWlpab78/tmzZ0kkEkycOJHKykoCgQA3b94kFovhdDqZMmUKFy5cYObMmbS1tREKhfjy5QsAx44d++kaSRcvXqSxsZFoNIrT6Syq5atFRqNlEURsEggEaGlpKcjr1PD17bzu7m5aW1sLcjwRu2iqTcQm06ZNIxKJFOwD0lOnTjF16tQCRCZiL414RETEVhrxiIiIrZR4RETEVko8IiJiKyUeERGxlRKPiIjY6g9+mjlpCUcGRgAAAABJRU5ErkJggg==\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "iter = 0\n",
    "max_epochs = 100\n",
    "fehler = np.zeros(max_epochs)\n",
    "\n",
    "# training\n",
    "while iter < max_epochs:\n",
    "    for x, label in zip(feature, labels):\n",
    "        delta = label - perzeptron(w, x)\n",
    "        if delta != 0: # falsch klassifiziert\n",
    "            fehler[iter] += 1\n",
    "            w_old = w.copy()\n",
    "            w += (delta * x)\n",
    "    \n",
    "    if fehler[iter] == 0:\n",
    "        visualize(feature, labels, w)\n",
    "        break\n",
    "\n",
    "    iter += 1\n",
    "else:\n",
    "    print(\"Es wurde keine Lösung gefunden.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(range(max_epochs), fehler)\n",
    "plt.xlabel('Epochen')\n",
    "plt.ylabel('Falsche Klassifikationen')\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## Aufgabe\n",
    "### Datensatz erstellen"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma = 0.7\n",
    "hunde = 26\n",
    "sonst = 53\n",
    "\n",
    "X1 = sigma * np.random.randn(hunde, 2) + (3.3, 4.42)\n",
    "X2 = sigma * np.random.randn(sonst, 2) + (1.4, 2.82)\n",
    "feature = np.concatenate((X1, X2))\n",
    "label = np.concatenate((np.ones(hunde), np.zeros(sonst)))\n",
    "\n",
    "plt.scatter(feature[:,0], feature[:,1], c=label)\n",
    "plt.xlabel('Grösse [cm]')\n",
    "plt.ylabel('Breite [cm]')\n",
    "plt.title('Trainingsdaten')\n",
    "plt.show()"
   ]
  }
 ]
}